Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

• MNIST
• CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x1f4876c56d8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x1f4877ade80>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

• model_inputs
• discriminator
• generator
• model_loss
• model_opt
• train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

• Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
• Z input placeholder with rank 2 using z_dim.
• Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, shape=(None, z_dim), name = 'input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return (input_real, input_z, learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

tf.reset_default_graph()

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        #28x28x3 ---> 14, 14, 128
        conv1 = tf.layers.conv2d(images, 128, 4, strides=2, padding='same')
        relu1 = tf.maximum(alpha * conv1, conv1)
        assert conv1.get_shape()[1:] == (14, 14, 128), conv1.get_shape()
        
        #14, 14, 128 ---> 7, 7, 256
        conv2 = tf.layers.conv2d(relu1, 256, 4, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(conv2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        assert bn2.get_shape()[1:] == (7, 7, 256), bn2.get_shape()
        
        #7, 7, 256 ----> 4, 4, 512
        conv3 = tf.layers.conv2d(relu2, 512, 4, strides=2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3, training=True)
        relu3 = tf.maximum(alpha * conv3, conv3)
        assert conv3.get_shape()[1:] == (4, 4, 512), conv3.get_shape()

        # Flatten it
        #flat = tf.contrib.layers.flatten(relu3)
        flat = tf.reshape(relu3, (-1, 4*4*256))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

tf.reset_default_graph()
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=not is_train):
        #fully connected layer
        conv1 = tf.layers.dense(z, 2 * 2 * 512)
        
        # first reverse convolution  -> 2x2x512
        conv1 = tf.reshape(conv1, (-1, 2, 2, 512))
        conv1 = tf.layers.batch_normalization(conv1, training=is_train)
        conv1 = tf.maximum(alpha * conv1, conv1)
        assert conv1.get_shape()[1:] == (2, 2, 512), conv1.get_shape()
        
        # second reverse convolution, -> 7x7x256
        conv2 = tf.layers.conv2d_transpose(conv1, 256, 5, strides=2, padding='valid')
        conv2 = tf.layers.batch_normalization(conv2, training=is_train)
        conv2 = tf.maximum(alpha * conv2, conv2)
        assert conv2.get_shape()[1:] == (7, 7, 256), conv2.get_shape()
        
        # third reverse convolution, -> 14x14x128
        conv3 = tf.layers.conv2d_transpose(conv2, 128, 5, strides=2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3, training=is_train)
        conv3 = tf.maximum(alpha * conv3, conv3)
        assert conv3.get_shape()[1:] == (14, 14, 128), conv3.get_shape()
        
        # Output layer
        logits = tf.layers.conv2d_transpose(conv3, out_channel_dim, 5, strides=2, padding='same')
        assert logits.get_shape()[1:] == (28, 28, out_channel_dim), logits.get_shape()
        
        out = tf.tanh(logits)
        return out

    
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

• discriminator(images, reuse=False)
• generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.9
    
    # Build the model
    g_model = generator(input_z, out_channel_dim, is_train=True)
   
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    
    # Calculate losses
    d_loss_real = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                              labels=tf.ones_like(d_logits_real) * smooth))
    d_loss_fake = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                              labels=tf.zeros_like(d_logits_real)))
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
                 tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                         labels=tf.ones_like(d_logits_fake) * smooth))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get the trainable_variables, split into G and D parts
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

• model_inputs(image_width, image_height, image_channels, z_dim)
• model_loss(input_real, input_z, out_channel_dim)
• model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _, image_width, image_height, image_channels = data_shape
    
    input_real, input_z, learn_rate = model_inputs(image_width, image_height, image_channels, z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            d_losses = []
            g_losses = []
            for b, batch_images in enumerate(get_batches(batch_size)):
                # TODO: Train Model
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _, dis_loss = sess.run([d_train_opt, d_loss], feed_dict={input_real: batch_images * 2, input_z: batch_z, learn_rate: learning_rate})
                _, gen_loss = sess.run([g_train_opt, g_loss], feed_dict={input_real: batch_images * 2, input_z: batch_z, learn_rate: learning_rate})
                
                d_losses.append(dis_loss)
                g_losses.append(gen_loss)
                
                print('Batch: {} - Discriminator Loss: {} - Generator Loss: {}'.format(b, dis_loss, gen_loss))
                
                if b % 100 == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 128  #32
z_dim = 100
learning_rate = 0.001
beta1 = 0.2

tf.reset_default_graph()


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Batch: 0 - Discriminator Loss: 1.5490458011627197 - Generator Loss: 15.471494674682617

Batch: 1 - Discriminator Loss: 20.363542556762695 - Generator Loss: 14.167376518249512
Batch: 2 - Discriminator Loss: 7.0914201736450195 - Generator Loss: 0.7734339237213135
Batch: 3 - Discriminator Loss: 10.071846008300781 - Generator Loss: 9.039796829223633
Batch: 4 - Discriminator Loss: 0.9290845990180969 - Generator Loss: 4.645222187042236
Batch: 5 - Discriminator Loss: 3.8752050399780273 - Generator Loss: 11.99703311920166
Batch: 6 - Discriminator Loss: 4.18848991394043 - Generator Loss: 3.776157855987549
Batch: 7 - Discriminator Loss: 3.4838597774505615 - Generator Loss: 8.02049446105957
Batch: 8 - Discriminator Loss: 1.8821690082550049 - Generator Loss: 2.6427621841430664
Batch: 9 - Discriminator Loss: 4.15925407409668 - Generator Loss: 7.363731861114502
Batch: 10 - Discriminator Loss: 2.9862048625946045 - Generator Loss: 2.634584426879883
Batch: 11 - Discriminator Loss: 1.793729543685913 - Generator Loss: 4.570260047912598
Batch: 12 - Discriminator Loss: 1.8435049057006836 - Generator Loss: 2.0969552993774414
Batch: 13 - Discriminator Loss: 2.832643508911133 - Generator Loss: 5.944619178771973
Batch: 14 - Discriminator Loss: 2.285851240158081 - Generator Loss: 1.2229409217834473
Batch: 15 - Discriminator Loss: 2.461012840270996 - Generator Loss: 3.9743504524230957
Batch: 16 - Discriminator Loss: 2.369658946990967 - Generator Loss: 1.397281527519226
Batch: 17 - Discriminator Loss: 2.6118381023406982 - Generator Loss: 2.6802053451538086
Batch: 18 - Discriminator Loss: 1.651123046875 - Generator Loss: 1.4812648296356201
Batch: 19 - Discriminator Loss: 1.3763346672058105 - Generator Loss: 1.6781091690063477
Batch: 20 - Discriminator Loss: 1.3132394552230835 - Generator Loss: 2.5713582038879395
Batch: 21 - Discriminator Loss: 1.7563536167144775 - Generator Loss: 1.9602086544036865
Batch: 22 - Discriminator Loss: 1.8851186037063599 - Generator Loss: 2.064662456512451
Batch: 23 - Discriminator Loss: 1.8644459247589111 - Generator Loss: 2.129530668258667
Batch: 24 - Discriminator Loss: 1.5765507221221924 - Generator Loss: 2.0941710472106934
Batch: 25 - Discriminator Loss: 1.2047699689865112 - Generator Loss: 1.5275686979293823
Batch: 26 - Discriminator Loss: 1.2910568714141846 - Generator Loss: 2.337907075881958
Batch: 27 - Discriminator Loss: 1.3573155403137207 - Generator Loss: 2.1049561500549316
Batch: 28 - Discriminator Loss: 1.3912689685821533 - Generator Loss: 2.2935900688171387
Batch: 29 - Discriminator Loss: 1.311486840248108 - Generator Loss: 2.123988628387451
Batch: 30 - Discriminator Loss: 1.447791337966919 - Generator Loss: 2.663438081741333
Batch: 31 - Discriminator Loss: 1.5045973062515259 - Generator Loss: 1.9818583726882935
Batch: 32 - Discriminator Loss: 1.0387786626815796 - Generator Loss: 2.296215534210205
Batch: 33 - Discriminator Loss: 1.0281404256820679 - Generator Loss: 2.1919074058532715
Batch: 34 - Discriminator Loss: 1.1403791904449463 - Generator Loss: 2.2943649291992188
Batch: 35 - Discriminator Loss: 1.4471434354782104 - Generator Loss: 2.1677401065826416
Batch: 36 - Discriminator Loss: 1.4947047233581543 - Generator Loss: 2.886533260345459
Batch: 37 - Discriminator Loss: 1.5682942867279053 - Generator Loss: 2.278599262237549
Batch: 38 - Discriminator Loss: 1.6698110103607178 - Generator Loss: 4.765143871307373
Batch: 39 - Discriminator Loss: 1.6419990062713623 - Generator Loss: 1.7224454879760742
Batch: 40 - Discriminator Loss: 0.8496444225311279 - Generator Loss: 2.888948440551758
Batch: 41 - Discriminator Loss: 0.7530678510665894 - Generator Loss: 1.4614018201828003
Batch: 42 - Discriminator Loss: 1.1633069515228271 - Generator Loss: 4.0071024894714355
Batch: 43 - Discriminator Loss: 1.6251670122146606 - Generator Loss: 0.8225813508033752
Batch: 44 - Discriminator Loss: 1.9200974702835083 - Generator Loss: 4.812820911407471
Batch: 45 - Discriminator Loss: 1.8533984422683716 - Generator Loss: 1.5880837440490723
Batch: 46 - Discriminator Loss: 1.3709297180175781 - Generator Loss: 4.393609046936035
Batch: 47 - Discriminator Loss: 1.4152169227600098 - Generator Loss: 1.8568025827407837
Batch: 48 - Discriminator Loss: 1.0639927387237549 - Generator Loss: 3.1432862281799316
Batch: 49 - Discriminator Loss: 0.988886833190918 - Generator Loss: 1.8086258172988892
Batch: 50 - Discriminator Loss: 1.6080394983291626 - Generator Loss: 4.288939952850342
Batch: 51 - Discriminator Loss: 1.8217326402664185 - Generator Loss: 1.0474393367767334
Batch: 52 - Discriminator Loss: 1.7374134063720703 - Generator Loss: 3.8549306392669678
Batch: 53 - Discriminator Loss: 1.5798683166503906 - Generator Loss: 1.0867277383804321
Batch: 54 - Discriminator Loss: 1.3652125597000122 - Generator Loss: 2.519639492034912
Batch: 55 - Discriminator Loss: 1.0643739700317383 - Generator Loss: 1.327797293663025
Batch: 56 - Discriminator Loss: 1.07034170627594 - Generator Loss: 2.611351251602173
Batch: 57 - Discriminator Loss: 0.9824597835540771 - Generator Loss: 1.4147156476974487
Batch: 58 - Discriminator Loss: 1.2594369649887085 - Generator Loss: 3.509688377380371
Batch: 59 - Discriminator Loss: 1.1473549604415894 - Generator Loss: 1.4339231252670288
Batch: 60 - Discriminator Loss: 1.6577301025390625 - Generator Loss: 5.092557430267334
Batch: 61 - Discriminator Loss: 1.3251638412475586 - Generator Loss: 1.2025858163833618
Batch: 62 - Discriminator Loss: 1.376535415649414 - Generator Loss: 3.3284339904785156
Batch: 63 - Discriminator Loss: 1.1232151985168457 - Generator Loss: 1.341568946838379
Batch: 64 - Discriminator Loss: 0.9404802322387695 - Generator Loss: 2.22445011138916
Batch: 65 - Discriminator Loss: 0.9107868671417236 - Generator Loss: 1.6515545845031738
Batch: 66 - Discriminator Loss: 1.26186203956604 - Generator Loss: 2.8916807174682617
Batch: 67 - Discriminator Loss: 1.4883114099502563 - Generator Loss: 1.4783532619476318
Batch: 68 - Discriminator Loss: 1.4193673133850098 - Generator Loss: 3.6263065338134766
Batch: 69 - Discriminator Loss: 1.5372116565704346 - Generator Loss: 1.5474495887756348
Batch: 70 - Discriminator Loss: 1.592289924621582 - Generator Loss: 5.275474548339844
Batch: 71 - Discriminator Loss: 1.6681878566741943 - Generator Loss: 0.8961522579193115
Batch: 72 - Discriminator Loss: 1.2994493246078491 - Generator Loss: 2.5649678707122803
Batch: 73 - Discriminator Loss: 1.1586569547653198 - Generator Loss: 1.0599946975708008
Batch: 74 - Discriminator Loss: 1.184408187866211 - Generator Loss: 2.435122013092041
Batch: 75 - Discriminator Loss: 1.217052698135376 - Generator Loss: 0.9875829219818115
Batch: 76 - Discriminator Loss: 1.1101667881011963 - Generator Loss: 2.651470184326172
Batch: 77 - Discriminator Loss: 0.9068196415901184 - Generator Loss: 1.4857819080352783
Batch: 78 - Discriminator Loss: 0.8434339165687561 - Generator Loss: 2.943526268005371
Batch: 79 - Discriminator Loss: 0.722149133682251 - Generator Loss: 1.9944674968719482
Batch: 80 - Discriminator Loss: 0.8280514478683472 - Generator Loss: 3.136702299118042
Batch: 81 - Discriminator Loss: 0.8497717976570129 - Generator Loss: 1.7287148237228394
Batch: 82 - Discriminator Loss: 1.7032406330108643 - Generator Loss: 4.798272132873535
Batch: 83 - Discriminator Loss: 2.0876569747924805 - Generator Loss: 1.010849118232727
Batch: 84 - Discriminator Loss: 1.5606269836425781 - Generator Loss: 3.724182605743408
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CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 128
z_dim = 100
learning_rate = 0.0002    #0.0001 
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 16

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Batch: 0 - Discriminator Loss: 1.5973303318023682 - Generator Loss: 2.909667491912842

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Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.